code: def _execute_shuffle_map(cls, ctx, op):
      out = op.outputs[0]
      shuffle_size = op.shuffle_size
      subset = op.subset
  
      inp = ctx[op.input.key]
      if subset is not None:
          result = inp[subset].copy()
      else:
          result = inp.copy()
      if result.ndim == 1:
          name = result.name
          result = result.to_frame()
          if name is None:
              result.columns = ["_duplicated_"]
          subset = result.columns.tolist()
      else:
          if subset is None:
              subset = result.columns.tolist()
          if len(subset) == 1:
              result.columns = subset = ["_duplicated_"]
      result["_chunk_index_"] = out.index[0]
      result["_i_"] = np.arange(result.shape[0])
      hashed = hash_dataframe_on(result, subset, shuffle_size)
      for i, data in enumerate(hashed):
          reducer_idx = (i,) + out.index[1:]
          ctx[out.key, reducer_idx] = result.iloc[data]
  
imports: import numpy
  from ...None import opcodes
  from ...core import OutputType
  from ...core.operand import OperandStage
  from ..utils import gen_unknown_index_value, hash_dataframe_on
  from ._duplicate import DuplicateOperand, validate_subset
label_desc: Use image node_id 16 for calling the DataFrameDuplicated obj's underlying member method code with example usage: obj._execute_shuffle_map(cls, ctx, op) without return types